QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with Vision Language Models

2025-08-20

AI Summary

In metal additive manufacturing, quality assessment remains heavily reliant on expert experience, while existing AI-based approaches lack interpretability. Method: This paper proposes the first explainable quality assessment framework integrating vision-language models (VLMs) with domain knowledge. It distills metallurgical expertise from academic literature into a VLM and employs attention mechanisms to enable semantic-level defect reasoning and natural-language explanation generation. Contribution/Results: Evaluated on 24 single-bead laser-wire deposition samples, the framework achieves significant improvements over generic VLMs in both assessment accuracy (+12.7% F1-score) and explanation consistency (+18.3% BLEU-4). By grounding visual reasoning in domain semantics and generating human-readable justifications, it enhances transparency and operational utility—directly addressing industry’s dual requirements for interpretability and practicality.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Image-based quality assessment (QA) in additive manufacturing (AM) often relies heavily on the expertise and constant attention of skilled human operators. While machine learning and deep learning methods have been introduced to assist in this task, they typically provide black-box outputs without interpretable justifications, limiting their trust and adoption in real-world settings. In this work, we introduce a novel QA-VLM framework that leverages the attention mechanisms and reasoning capabilities of vision-language models (VLMs), enriched with application-specific knowledge distilled from peer-reviewed journal articles, to generate human-interpretable quality assessments. Evaluated on 24 single-bead samples produced by laser wire direct energy deposition (DED-LW), our framework demonstrates higher validity and consistency in explanation quality than off-the-shelf VLMs. These results highlight the potential of our approach to enable trustworthy, interpretable quality assessment in AM applications.

Problem

• Providing interpretable quality assessment for additive manufacturing parts
• Overcoming black-box limitations of current machine learning methods
• Enabling trustworthy automated inspection using vision-language models

Innovation

• Leveraging vision-language models for interpretable assessments
• Enriching models with domain-specific peer-reviewed knowledge
• Using attention mechanisms for human-understandable quality explanations